Data Compression Using Nearest Neighbor Cluster

3. The method of claim 1, wherein the locality sensitive hashing function is a secure hash algorithm 1 (“SHA-1”) or a Message Digest 5 (“MD5”) algorithm.

4. The method of claim 1, wherein the cluster is created in an unsupervised learning environment.

5. The method of claim 1, wherein the cluster is created using an offload engine.

8. The method of claim 1, wherein, instead of using locality sensitive hashing to create the cluster of nearest neighbors, one or more of the following algorithms has been used to create the cluster of nearest neighbors: a k-means clustering algorithm, a k-medoids clustering algorithm, a mean shift algorithm, a generalized method of moment (GMM) algorithm, or a density based spatial clustering of applications with noise (DBSCAN) algorithm.

11. The system of claim 9, wherein the locality sensitive hashing function is a secure hash algorithm 1 (“SHA-1”) or a Message Digest 5 (“MD5”) algorithm.

12. The system of claim 9, wherein the cluster is created in an unsupervised learning environment.

13. The system of claim 9, wherein the cluster is created using the offload engine.

16. The system of claim 9, wherein, instead of using locality sensitive hashing to create the cluster of nearest neighbors, one or more of the following algorithms has been used to create the cluster of nearest neighbors: a k-means clustering algorithm, a k-medoids clustering algorithm, a mean shift algorithm, a generalized method of moment (GMM) algorithm, or a density based spatial clustering of applications with noise (DBSCAN) algorithm.

18. The non-transitory, computer readable medium of claim 17, wherein, instead of using locality sensitive hashing to create the cluster of nearest neighbors, one or more of the following algorithms has been used to create the cluster of nearest neighbors: a k-means clustering algorithm, a k-medoids clustering algorithm, a mean shift algorithm, a generalized method of moment (GMM) algorithm, or a density based spatial clustering of applications with noise (DBSCAN) algorithm.